The present subject matter is directed generally towards a system and method for estimating the location of a wireless mobile device that is in communication with a wireless communications network. Some embodiments of the present subject matter may estimate the location of a wireless mobile device using information from one or more Network Measurement Reports (“NMRs”) which may be generated by a wireless communications network or the mobile device.
As is well known in the art, the use of wireless communication devices such as telephones, pagers, personal digital assistants, laptop computers, anti-theft devices, etc., hereinafter referred to collectively as “mobile devices”, has become prevalent in today's society. Along with the proliferation of these mobile devices is the safety concern associated with the need to locate the mobile device, for example in an emergency situation. For example, the Federal Communication Commission (“FCC”) has issued a geolocation mandate for providers of wireless telephone communication services that puts in place a schedule and an accuracy standard under which the providers of wireless communications must implement geolocation technology for wireless telephones when used to make a 911 emergency telephone call (FCC 94-102 E911). In addition to E911 emergency related issues, there has been increased interest in technology which may determine the geographic position, or “geolocate” a mobile device. For example, wireless telecommunications providers are developing location-enabled services for their subscribers including roadside assistance, turn-by-turn driving directions, concierge services, location-specific billing rates and location-specific advertising.
Currently there are a number of different ways to geolocate a mobile device. For example, providers of wireless communication services have installed mobile device location capabilities into their networks. In operation, these network overlay location systems take measurements on radio frequency (“RF”) transmissions from mobile devices at base station locations surrounding the mobile device and estimate the location of the mobile device with respect to the base stations. Because the geographic location of the base stations is known, the determination of the location of the mobile device with respect to the base station permits the geographic location of the mobile device to be determined. The RF measurements of the transmitted signal at the base stations may include the time of arrival, the angle of arrival, the signal power, or the unique/repeatable radio propagation path (radio fingerprinting) derivable features. In addition, the geolocation systems may also use collateral information, e.g., information other than that derived for the RF measurement to assist in the geolocation of the mobile device, i.e., location of roads, dead-reckoning, topography, map matching, etc.
In a network-based geolocation system, the mobile device to be located is typically identified and radio channel assignments determined by (a) monitoring the control information transmitted on radio channel for telephone calls being placed by the mobile device or on a wireline interface to detect calls of interest, i.e., 911, (b) a location request provided by a non-mobile device source, i.e., an enhanced services provider. Once a mobile device to be located has been identified and radio channel assignments determined, the location determining system is first tasked to determine the geolocation of the mobile device and then directed to report the determined position to the requesting entity or enhanced services provider. The monitoring of the RF transmissions from the mobile device or wireline interfaces to identify calls of interest is known as “tipping,” and generally involves recognizing a call of interest being made from a mobile device and collecting the call setup information. Once the mobile device is identified and the call setup information is collected, the location determining system may be tasked to geolocate the mobile device.
While the above-described systems are useful in certain situations, there is a need to streamline the process in order to efficiently and effectively handle the vast amount of data being sent between the wireless communications network and the large number of mobile devices for which locations are to be determined. In this regard, embodiments of the present subject matter may overcome the limitations of the prior art by estimating the location of a wireless mobile device using Network Measurement Reports (“NMRs”) which include calibration data for a number of locations within a geographic region. An NMR may be, in one embodiment, a vector of measurement observations. Embodiments of the present subject matter may provide an accurate location of a mobile device using a variety of observations, measured at the mobile or by the network in relation to the mobile. Exemplary pattern matching schemes may employ these observations and/or associate such observations with a database containing previously made observations and/or synthesized calibration data to determine a location of a mobile device. Exemplary pattern matching schemes may be provided with one or more NMRs or a set of such measurements associated with a mobile device at its unknown location, a calibration database containing previously obtained measurements, predicted measurements, labels associated with such measurements and/or synthesized data indexed to location.
One conventional method of location estimation utilizing NMRs is to compare an NMR from a target mobile device with calibration vectors in a respective measurement database, determine the closest NMR (using some metric), and assign the location of the closest NMR as the location of the NMR for the target mobile device. Such a method, however, provides unacceptable errors, especially when the NMRs contain power measurements, as there may be a large variability in these measurements. This makes reliably locating the target mobile device difficult. Hence, some form of averaging is often desired. Another conventional method of location estimation utilizing NMRs may include dividing the location space (e.g., a region S containing all possible location solutions) in some manner into sub-regions Ci. Each sub-region Ci, may then be associated with a characterizing vector of measurements or a set of attributes. In this conventional method, by averaging multiple NMRs obtained in each such sub-region Ci, location accuracy may be improved. U.S. Pat. No. 6,496,701 to Chen provides such a technique. This technique is generally referred to as a disjoint partitioning of location space into regions or sub-regions Ci. For example, Chen describes an explicit disjoint partitioning of a location region (a cell S) into sub-cells Ci using the following relationship to divide the location space into in disjoint regions:S=Σi=1mCi  (1)
This conventional method of dividing location space is not ideal as there is little chance of obtaining characterizing vectors of observed measurements. For example, considering a user of a mobile device located at a window in a building, the act of the user turning around may result in observed measurements becoming dramatically different from signal loss, etc. Further, in a challenging environment such as an urban canyon, if a user travels five to ten feet, observed measurements may dramatically change due to blockage, multipath and/or reflections from the environment. Thus, the prior art is limited in determining the proper sub-cells or sub-regions Ci in such challenging environments.
Another conventional approach described in U.S. Patent Application Publication No. 20090117907 to Wigren and in the publication entitled, “Adaptive Enhanced Cell-ID Fingerprinting Localization by Clustering of Precise position Measurements,” September 2007, by Wigren, groups calibration data by similarity. The resulting groups or clusters are analyzed to generate a most likely polygonal shape in the location space. Further, these groups are made in location space and are groupings of measurements with a specified bin size for each measurement of interest. The polygons are determined as a function of their measurements, and the polygons are then used to specify likely areas or regions where one assigns a location for future measurements. Such an approach, however, is limited in determining an accurate location for a target mobile device in challenging environments.
Therefore, there is a need in the art for a system and method to improve location capabilities of a communications system in such environments. In some cases, it may not be possible to completely populate such a calibration database, e.g., in a dense urban environment it may not be possible to physically explore every few meters of every street or building to gather calibration data. Thus, there is also a need to determine whether such non-calibrated regions may be populated with synthetic calibration data whereby such a synthetic calibration database may perform similarly to a database populated with data from calibrated regions. There is also a need in the art to achieve a reasonable degree of location estimation accuracy in dense environments with no prior calibration effort.